time-series

Hi,

first thanks for this awesome software. But I have some trouble and I don't know how to proceed. First I try to find out what else I could provide for this report to be a good bug report.

First, I use the latest version (v1.14.0) from openSUSE build Service. I know, I should compile it by my self to avoid any other causes for this behavior. But for now I still use this package from there

Bug Description
In insert/special_character_show.py The spelling of the word should use English,not Chinese,.

To Reproduce
Steps to reproduce the behavior:

Go to 'https://github.com/taosdata/TDengine/blob/develop/tests/pytest/insert/special_character_show.py'

Expected Be

Relevant telegraf.conf:

[[inputs.openweathermap]]
  app_id = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
  

it's becoming more time-consuming and error-prone to manually re-test all the demos following internal refactorings and API adjustments.

now that the API is fleshed out a bit, it's possible to test a large amount of code (non-granularly) without having to simulate all interactions via Puppeteer or similar.

a lot of code can already be regression-tested by simply running all the demos and val

count(1) for queries does not work on UI

Describe the issue linked to the documentation

Some of the classifiers don't include and examples section in their docstring. We should update the docstrings to include the examples section.

• ColumnEnsembleClassifier
• ElasticEnsemble
• ProximityForest
• ProximityTree
• ProximityStump

提问题？
请把问题现象、相关日志、初步排查结果写清楚，即提供尽量多的线索，这样方便我们定位问题。

提建议？
请写明白建议原因，即为何有此建议。

提修复PR？
请说明为何做此修改，需要做好测试。

提Feature？
建议先将想法提出，不着急写代码，与社区同道沟通，大家觉得OK再做代码实现

---

另外，我们是在做开源软件，不是服务于您的企业，做您的乙方，您一分钱没付，就不要颐指气使了，认同我们这个社区，则参与进来，不认同，就不参与，谢谢理解

Required by DataGrip. See crate/crate#11726

The full query is

select L.transactionid::varchar::bigint as transaction_id
from pg_catalog.pg_locks L                                                                               
where L.transactionid is not null                                                                        
order by pg_catalog.age(L

Recently found your library and it is really great and handy!
The evaluation of the forecasting performance is best done using a cross-validation approach. The backtest_forecasting()-function does that - although it currently iterates and re-trains the model on every single time step. In my application, I am training ten-thousands of different time series and it becomes computationally unfeasib

Description

(A clear and concise description of what the feature is.)

• util.cumsum implementation https://github.com/awslabs/gluon-ts/blob/master/src/gluonts/mx/util.py#L326 does not scale under mx.ndarray
• cumsum is 2-5 times slower than nd.cumsum under both mx.sym and mx.ndarray, and even fails for large 4-dim input

Sample test

Code

# import ...
def test_

Hi, Thanks for the awesome library!

So I am running a Kmeans on lots of different datasets, which all have roughly four shapes, so I initialize with those shapes and it works well, except for just a few times. There are a few datasets that look different enough that I end up with empty clusters and the algorithm just hangs ("Resumed because of empty cluster" again and again).

I conceptually